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  This is are model weights originally provided by the authors of the paper [Text-Only Training for Image Captioning using Noise-Injected CLIP](https://arxiv.org/pdf/2211.00575.pdf).
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- There method aims to train fine-tune CLIP with only text samples. Therefore they are injecting zero-mean Gaussian Noise with a standard-deviation(STD) of into the text embeddings before decoding.
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  In their words:
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  *Specifically, we assume that the visual embedding corresponding to a text embedding
 
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  This is are model weights originally provided by the authors of the paper [Text-Only Training for Image Captioning using Noise-Injected CLIP](https://arxiv.org/pdf/2211.00575.pdf).
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+ Their method aims to train CLIP with only text samples. Therefore they are injecting zero-mean Gaussian Noise with a standard-deviation(STD) of into the text embeddings before decoding.
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  In their words:
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  *Specifically, we assume that the visual embedding corresponding to a text embedding